Harnessing Artificial Intelligence in Environmental Education for Sustainable Waste Management in Port Harcourt Metropolis

AbstractThis study explores the integration of Artificial Intelligence (AI) into environmental education as a strategy for promoting sustainable waste management in Port Harcourt Metropolis. Rapid urbanization and population growth in Port Harcourt have intensified waste generation, posing significant environmental and public health challenges. Despite various waste management initiatives, limited research has examined the potential of AI-driven educational tools to enhance community awareness and behavioural change in waste disposal practices. This study addresses this gap by investigating how AI can be harnessed to improve environmental knowledge, engagement, and sustainable waste management behaviour among residents. The research employs a mixed-methods approach, combining surveys, interviews, and observational analysis to assess the effectiveness of AI-supported educational interventions. Findings indicate that AI-enhanced learning platforms significantly increase participants’ understanding of waste management practices, encourage environmentally responsible behaviours, and facilitate data-driven decision-making for waste authorities. The study concludes that AI can serve as a transformative tool in environmental education, bridging knowledge gaps and fostering sustainable urban waste practices. Recommendations include the development of localized AI-powered educational programmes and the integration of AI analytics in municipal waste management planning to optimize efficiency and community participation.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-01
DOI
https://doi.org/10.5281/zenodo.17233324
Primary Topic
Canadian Policy and Governance
Type
article
Field-Weighted Citation Impact
0.00
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article

Harnessing Artificial Intelligence in Environmental Education for Sustainable Waste Management in Port Harcourt Metropolis

Zenodo (CERN European Organization for Nuclear Research)
Canadian Policy and Governance
article

Harnessing Artificial Intelligence in Environmental Education for Sustainable Waste Management in Port Harcourt Metropolis

article en

Abstract

AbstractThis study explores the integration of Artificial Intelligence (AI) into environmental education as a strategy for promoting sustainable waste management in Port Harcourt Metropolis. Rapid urbanization and population growth in Port Harcourt have intensified waste generation, posing significant environmental and public health challenges. Despite various waste management initiatives, limited research has examined the potential of AI-driven educational tools to enhance community awareness and behavioural change in waste disposal practices. This study addresses this gap by investigating how AI can be harnessed to improve environmental knowledge, engagement, and sustainable waste management behaviour among residents. The research employs a mixed-methods approach, combining surveys, interviews, and observational analysis to assess the effectiveness of AI-supported educational interventions. Findings indicate that AI-enhanced learning platforms significantly increase participants’ understanding of waste management practices, encourage environmentally responsible behaviours, and facilitate data-driven decision-making for waste authorities. The study concludes that AI can serve as a transformative tool in environmental education, bridging knowledge gaps and fostering sustainable urban waste practices. Recommendations include the development of localized AI-powered educational programmes and the integration of AI analytics in municipal waste management planning to optimize efficiency and community participation.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 39%
Canadian Policy and Governance
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Harnessing Artificial Intelligence in Environmental Education for Sustainable Waste Management in Port Harcourt Metropolis · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS